EFL Teachers’ Perceptions on Blackboard Applications
Bibliographic record
Abstract
The widespread availability of technological infrastructure has enhanced the adoption of learning management systems (LMSs) in educational institutions. Blackboard is one of the most popular marketable LMSs adopted in higher education institutions. As some previous studies have viewed that positive perceptions played a vital role in adopting new technologies, this paper aims to investigate teachers’ perceptions on blackboard applications in the context of teaching English as a foreign language (EFL). To gather data, 32 EFL university teachers from Saudi Arabia were surveyed and interviewed about their perceptions toward the use of the blackboard. The results from the data instruments reveal that EFL teachers have positive perceptions on Blackboard applications to English language teaching. Most teachers view Blackboard as a structured e-learning platform that helps improve the teacher-student relationship in a course and aids to make teaching English more successful. The study findings; however, revealed that the use of blackboard as a blending learning is still focusing on administrative issues rather than pedagogical significance for language learning. Recommendations and directions for future research are highlighted at the end of this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".